Compare CellRanger vs scUTRquant DimReds
Mervin Fansler
19 August 2021
Purpose
Here we plot PCA and UMAP dimensionality reductions of the UMI count data from CellRanger and scUTRquant for comparison. We expect highly similar projections since the underlying data is identical.
Initialization
Libraries
library(magrittr)
library(tidyverse)
library(cowplot)
library(Matrix)
library(HDF5Array)
library(SingleCellExperiment)
library(scater)Parameters
set.seed(20210818)
BiocParallel::register(BiocParallel::MulticoreParam(12))Functions
plot_dimred_compare <- function (sce_x, sce_y, dimred="PCA",
label_x="CellRanger", label_y="scUTRquant") {
gx <- plotReducedDim(sce_x, dimred)
gy <- plotReducedDim(sce_y, dimred)
glabx <- ggdraw() +
draw_label(label_x, fontface='bold', size=16, hjust=0.36, vjust=0.5) +
theme(plot.margin=margin(0, 0, 6, 0))
glaby <- ggdraw() +
draw_label(label_y, fontface='bold', size=16, hjust=0.36, vjust=0.5) +
theme(plot.margin=margin(0, 0, 6, 0))
plot_grid(gx, gy, glabx, glaby, nrow=2, rel_heights=c(1, 0.05))
}Data
Loading
df_counts_all <- readRDS("data/sce/processed/df_counts.all_genes.pca.Rds")Preprocessing
Compute UMAP Coordinates
df_counts_all %<>%
mutate(sce_sq=map(sce_sq, runUMAP),
sce_10x=map(sce_10x, runUMAP))Analysis
PCA
df_counts_all %>%
transmute(sample_id, g=map2(sce_10x, sce_sq, plot_dimred_compare, dimred="PCA")) %>%
deframe() %>% {
for (id in names(.)) {
title <- ggdraw() +
draw_label(id, fontface='bold', size=20, x=0.5, hjust=0.5) +
theme(plot.margin=margin(0, 0, 0, 0))
g <- plot_grid(title, .[[id]], ncol=1, rel_heights=c(0.1,1))
## print locally in this document
print(g)
## save to PDFs
# ggsave(sprintf("output/supplement/sup2-pca-compare-%s.pdf", id),
# .[[id]], width=5, height=4, dpi=300)
}
}
UMAP
df_counts_all %>%
transmute(sample_id, g=map2(sce_10x, sce_sq, plot_dimred_compare, dimred="UMAP")) %>%
deframe() %>% {
for (id in names(.)) {
title <- ggdraw() +
draw_label(id, fontface='bold', size=20, x=0.5, hjust=0.5) +
theme(plot.margin=margin(0, 0, 0, 0))
g <- plot_grid(title, .[[id]], ncol=1, rel_heights=c(0.1,1))
## print locally in this document
print(g)
## save to PDFs
# ggsave(sprintf("output/supplement/sup2-pca-compare-%s.pdf", id),
# .[[id]], width=5, height=4, dpi=300)
}
}
Conclusion
There is some structural similarity in the projections, but it is not immediately evident that exactly the same clusters are present. It is also not clear whether one mode is any more articulated than the other. In all samples, the first two PCs for CellRanger capture more variance than those for scUTRquant.
Runtime Details
Session Info
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Mojave 10.14.6
##
## Matrix products: default
## BLAS/LAPACK: /Users/mfansler/miniconda3/envs/bioc_3_12/lib/libopenblasp-r0.3.12.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] scater_1.18.0 SingleCellExperiment_1.12.0
## [3] SummarizedExperiment_1.20.0 Biobase_2.50.0
## [5] GenomicRanges_1.42.0 GenomeInfoDb_1.26.0
## [7] HDF5Array_1.18.0 rhdf5_2.34.0
## [9] DelayedArray_0.16.0 IRanges_2.24.0
## [11] S4Vectors_0.28.0 MatrixGenerics_1.2.0
## [13] matrixStats_0.58.0 BiocGenerics_0.36.0
## [15] Matrix_1.3-2 cowplot_1.1.0
## [17] forcats_0.5.1 stringr_1.4.0
## [19] dplyr_1.0.5 purrr_0.3.4
## [21] readr_1.4.0 tidyr_1.1.3
## [23] tibble_3.1.0 ggplot2_3.3.3
## [25] tidyverse_1.3.0 magrittr_2.0.1
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-6 fs_1.5.0
## [3] lubridate_1.7.10 RcppAnnoy_0.0.18
## [5] httr_1.4.2 tools_4.0.2
## [7] backports_1.2.1 irlba_2.3.3
## [9] utf8_1.1.4 R6_2.5.0
## [11] vipor_0.4.5 uwot_0.1.10
## [13] DBI_1.1.1 colorspace_2.0-0
## [15] rhdf5filters_1.2.0 withr_2.4.1
## [17] gridExtra_2.3 tidyselect_1.1.0
## [19] compiler_4.0.2 cli_2.3.1
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## [23] xml2_1.3.2 labeling_0.4.2
## [25] scales_1.1.1 digest_0.6.27
## [27] rmarkdown_2.7 XVector_0.30.0
## [29] pkgconfig_2.0.3 htmltools_0.5.1.1
## [31] sparseMatrixStats_1.2.0 highr_0.8
## [33] dbplyr_2.1.0 rlang_0.4.10
## [35] readxl_1.3.1 rstudioapi_0.13
## [37] FNN_1.1.3 DelayedMatrixStats_1.12.0
## [39] farver_2.1.0 generics_0.1.0
## [41] jsonlite_1.7.2 BiocParallel_1.24.0
## [43] BiocSingular_1.6.0 RCurl_1.98-1.2
## [45] GenomeInfoDbData_1.2.4 scuttle_1.0.0
## [47] ggbeeswarm_0.6.0 Rcpp_1.0.6
## [49] munsell_0.5.0 Rhdf5lib_1.12.0
## [51] fansi_0.4.2 viridis_0.5.1
## [53] lifecycle_1.0.0 stringi_1.5.3
## [55] yaml_2.2.1 zlibbioc_1.36.0
## [57] grid_4.0.2 crayon_1.4.1
## [59] lattice_0.20-41 haven_2.3.1
## [61] beachmat_2.6.4 hms_1.0.0
## [63] knitr_1.31 pillar_1.5.1
## [65] codetools_0.2-18 reprex_1.0.0
## [67] glue_1.4.2 evaluate_0.14
## [69] modelr_0.1.8 vctrs_0.3.6
## [71] cellranger_1.1.0 gtable_0.3.0
## [73] assertthat_0.2.1 xfun_0.20
## [75] rsvd_1.0.3 broom_0.7.5
## [77] RSpectra_0.16-0 viridisLite_0.3.0
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